A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

S. Y. Yang; Siu Lau Ho; G. Z. Ni; José Márcio Machado; K. F. Wong

doi:10.1109/CEFC-06.2006.1632955

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

S. Y. Yang, Siu Lau Ho, G. Z. Ni, José Márcio Machado, K. F. Wong

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm.

Original language	English
Title of host publication	12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006
DOIs	https://doi.org/10.1109/CEFC-06.2006.1632955
Publication status	Published - 21 Nov 2006
Event	12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006 - Miami, FL, United States Duration: 30 Apr 2006 → 3 May 2006

Conference

Conference	12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006
Country/Territory	United States
City	Miami, FL
Period	30/04/06 → 3/05/06

ASJC Scopus subject areas

Engineering(all)

More information

10.1109/CEFC-06.2006.1632955

http://hdl.handle.net/10397/1102

Cite this

@inproceedings{e72b1af65bce4fcca7816897cf924f27,

title = "A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics",

abstract = "To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm.",

author = "Yang, {S. Y.} and Ho, {Siu Lau} and Ni, {G. Z.} and Machado, {Jos{\'e} M{\'a}rcio} and Wong, {K. F.}",

year = "2006",

month = nov,

day = "21",

doi = "10.1109/CEFC-06.2006.1632955",

language = "English",

isbn = "1424403200",

booktitle = "12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006",

note = "12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006 ; Conference date: 30-04-2006 Through 03-05-2006",

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Yang, SY, Ho, SL, Ni, GZ, Machado, JM & Wong, KF 2006, A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics. in 12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006., 1632955, 12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006, Miami, FL, United States, 30/04/06. https://doi.org/10.1109/CEFC-06.2006.1632955

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics. / Yang, S. Y.; Ho, Siu Lau; Ni, G. Z. et al.
12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006. 2006. 1632955.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Wong, K. F.

PY - 2006/11/21

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N2 - To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm.

AB - To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm.

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M3 - Conference article published in proceeding or book

SN - 1424403200

SN - 9781424403202

BT - 12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006

T2 - 12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006

Y2 - 30 April 2006 through 3 May 2006

ER -

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

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